{
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   "cell_type": "markdown",
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   "source": [
    "# An Introduction to Bias-Variance\n",
    "\n",
    "## Introduction\n",
    "\n",
    "<p>This notebook gives a foundational overview of the Bias-Variance tradeoff,  one of the most important topics related to predictive modeling for Data Science. The Bias-Variance Tradeoff in short is a framework for understanding where error comes from in a supervised learning model. There is a famous quote attributed to statistician George Box that goes:<br><br>\n",
    "\n",
    "<i><center>\"All models are wrong, though some are useful.</center></i><br>\n",
    "\n",
    "The word \"model\" alone implies some simplification of the world, and such simplifications generally accept a level of misrepresentation for the sake of parsimony (ie., simplicity). The challenge we face as Data Scientists is that the world we are usually trying to model produces data that is inherently noisy. Modeling such noisy systems requires a delicate balancing act between model parsimony and generalizability. When our measurement systems are rife with uncertainty, the desire to build simpler models isn't just a stylistic choice - it is an essential practice if we desire to successfully generalize on unknown data.<br><br>\n",
    "\n",
    "The Bias-Variance tradeoff is a theoretical concept that fortunately can be very intuitive and easy to illustrate. This notebook aims to offer the reader both views. On the theory side, we'll define and explain some core constructs and show how the common least squares error can be decomposed into both bias and variance components. To make the concepts more intuitive, we'll present multiple illustrations with simulated data. The rest of this notebook is organized as follows:<br>\n",
    "\n",
    "\n",
    "<ul>\n",
    "    <li>Explore and define Bias and Variance with a focus on illustration.</li>\n",
    "    <li>Derive the Bias-Variance decomposition for least squares error.</li>\n",
    "    <li>Discuss practical considerations that result from the Bias-Variance decomposition.</li>\n",
    "</ul>\n",
    "</p>\n",
    "\n",
    "## Intuitive Explanation of Bias and Variance.\n",
    "<p>Both \"bias\" and \"variance\" are common terms in statistics, and their general meaning isn't that far from how they are used in statistical learning theory. If you recall in statistics, the bias of an estimator is defined as the difference between the expected value of an estimator and the true value of the quantity being estimated. The \"bias\" and \"variance\" of a predictive model follow the same idea since a \"model\" is really just some function that estimates the expected value of an outcome $Y$ conditional on some $X=x$,  (i.e., $f(x)=E[Y|X=x]$).\n",
    "\n",
    "<br><br>\n",
    "\n",
    "So to get closer to an intuitive explanation, let's start by looking at some simulated data. The following plots show a polymonial scatter created with the following data generating process:<br><br>\n",
    "\n",
    "<center>$Y=\\beta^T \\: K^d(X) + \\epsilon$</center><br>\n",
    "\n",
    "Where $X$ is a single numeric variable, $K^d(X)$ is a $d$-degree polynomial kernel on $X$ (i.e., $K^3(X)$ admits a vector $<X^3, X^2, X>$), $\\beta$ is a $d$-length coefficient vector and $\\epsilon$ is a normal random variable with $0$-mean and variance $\\sigma^2$.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1193ff780>]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append(\"./utils/\")\n",
    "import numpy as np\n",
    "import os\n",
    "import bias_variance as bv\n",
    "import imp\n",
    "imp.reload(bv)\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "b = [-10, 2, -0.04, 0.000275]\n",
    "d = bv.simPolynomial(sigma = 20, betas = b, n = 40)\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.plot(d['x'], d['y'], 'b.')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>The points above follow some sort of trend, but given the level of noise, it is hard to trace exactly what that trend is. Imagine you didn't know the data generating function and that someone gave you a piece of string and asked you to lay the string out so that it best captures the trend you see in the data. The string, once laid out, is your mental model of how this data was generated. Although a piece of string is infinitely flexible, imagine that you are required in advance to specify exactly how many times your piece of string is allowed to bend. If your only task was to draw a trend line that was as close to as many points above as possible, you'd likely choose the infinitely flexible option. Your game master here reveals though that there is another set of points hidden from you, and the real task is to have your string be as close to the hidden points as possible (which we'll assume will at least come from the same data generating process). With this revelation, you'd have to ask yourself whether you prefer a string with fewer flex points, that sits near a safe average of any cluster of points, or the infinitely flexible string that can possibly go through all points. This is the choice that every modeler has to make, and understanding both bias and variance will help guide us towards making better modeling choices.\n",
    "\n",
    "</p>\n",
    "\n",
    "#### Deeper Look at Bias\n",
    "<p>\n",
    "Your choice will likely depend on how confident you are that the points you see really represent the world of such points. Your confidence in that department is likely proportional to the number of points you can see here. Even if you had an infinite number of points, the string with fewer flex points might never be able to trace the true trend in the data. We'll illustrate this with the following chart, using a straight line (string with zero flexibility) as our model.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lpfbbdKsEhC7ddhsGVFfHRURODmPTpiWKPnFHNH4d33ba6YOt4m9VOpzars47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2CEKdFAMVhP5xExEi3wzP4/c8xxxzL/aAMjNEpmcoDBwX6SyQEMo0SxFGDcTrlE/6kGBtOt9GXb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\ntTMz6XRTkw9jO+mf7KgsM0N2UYWGh1cnHu1UQtmJZNyu39A+maEb02tK9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SIAACAASURB\nVH7U5NS6ZqQsamGkVEt/8fFl3lbpfdeagumEEiWXzU6+tOKyE16rbVH77c2unbP6rivTk/82RnIp\n49EyVo2mSmnd01srZtRemp1aqZaumTS14pdv/mIUp/Ld1PuNfblup36bfcZoswpfMKpz/sKf6TrV\nhjHOEBLGjlsO3AVgf+mubC/rhQ0lY+e3vFzKyjzr1Wko5zvrKchanYzyutZUNWmjgV69kmjwoOG0\nZ9Mmen3AABr3l240pc00+mXoKDrYN4n2tWvv7aG5qTVldu9NuU8No/0vTqID/55HqTO3UtHhPHId\nKtaUx2xnofwuKYhqa5iWLSOqUkV9vY5kkKhtCKAng1Jh11LmlP/NdLpyo0vZcZlt4MzkQ8sLphaX\nndE5vXjlv8Wpi6do+5Ht9PnPn9Pzm5+n3ov60E2ftfEYNp3n/oUeX/s4vfLtVOr0yCraknGAiq8U\nl0tHz4CQvvuqUKopEVq/rZpyZqR4G+VBbzMCrWtGU06M4pU/I7+v5z1UXlOub/PFA6L8Li87M54d\ns22NGVmsGM/+Rtmeao0qm23jrHop1PolaZc2efsqhTPjZdUbQNB7h5TTqNTWFJo1vp1oR/TS1MqP\nJLsZfBlMsCKz1WfN4sS6qspIINoVxhwhY+xY+QslY4eovLKppTjI/+S72SjDSlPVCgoKPGkcOeKe\nqlazZk2qGxFBnf70J5rc60469MZE+uXJ52hN3H2U3jbB26BpdRMd6NmLcoYOpYI3JtKBD+bS2e+2\nUPaOXHIdulxOBmWj7ktDKs+PtGuS2ranUjrNm2t3LnY6L0mpUsqjp3BqKWHKeNWmKinj8xWpczG7\nDsXKtDMpnPK3LSkpoYJzBfTFro3U8dkZ9MRXz1CPxT281tfctqA7jdwwkiZumk4bcjZQ/tl8crlK\nysmhTMeMoWFnionympZBYMbYNTMqrSezlH5srLX1Q2aUceX7oxVOz9Mlb3vkMkvbOOvVabP50DPc\n9K4Z1QOzBoCZa3ICsUZAr76Y+e314rMqh1q5yrfSJypvkFqRT/6+6/2GyntmNxYxwok+S++er5so\n+AMr8dptb4nY0GHCGzZ2HEKuBOut7VBOk1HrEIYPH05/iqhKEwYMoFMrVlDW6x9Q3uh/0H87dKQd\nMc29DZrYVpR1Rw9KvecJ+k/863Tgvdl0dvNmKnK5qOTSJa945fItW1a2ZauacSHPl5aCKM+bFpIX\nZNmy8gu15WUmn69rprPUQ2t6gZ4yqFzga2a0XX5dyyiz22HLZTITXv7bmnmu+Eoxbc04SF8f/Jre\n+eEdeuKbJ+gv87p6jJrWs1rTPcvuoadXj6GOIz6l5bu/p5+yThpO89Ma/TRTnr4YanpxKA0uJ5Ry\nre/btrlHydXW9OilY4SeoqWnRMqv63mn5AM1vuCEgqqnQJuNw4xcdtoaM2WtJ5dVxVRtYMWuMaiH\n1E6bGfAxStfKOVtSHE5O9zEru9azWteD4d1w0kDSq4v+MsQYJlRgY8dBjBQuead65MgR6tW1K+1a\n+F86tXIVHfv3dHo7KpoWNLqBtikMmr03tqDMxO70ZdsEmtSkO60d8Sb1bZhM/W65i7rdWlROydVq\n7KX0pfAff+ztPVDrQKURZaUxJ8WltUWpmuESH69u1CjT1jIYrUzp0jvnRTm6ZbT2w8gAkk8FMVoD\npIxfS7GzqhTpGRZFxUW097e9tOSXJfT69tdp4NcDvQ7mbDe7Hd27pB8lvDie/vP9fNp9dLfnYE61\nstDKi1wWs0qeXj0wyrMyDr1n7SiH0m9rVRGQGw9Gv69VhdCMAa8Xhx7btrnX1Jg10pxWxPQw42kw\nY+hpPWOmPikNRrW1HGrxG70zelhZl2bHYFO77ouRYsYwVUvDn0q9lee0phVK8Vltp3zBaSNQijMQ\n6TiJ2fcn1OQPBxkrE2zsOIhaZ+HKOEuH1u+lZ9p9Ra+3mUH7R7xA2Q/2p7Q4xTk0LWMpMzGRvmzT\nkcZfF0WP1K1HPevWp2fvu49+2JbjFbdaByv/rzYtQ3lduvfxx94GiBKl10W6JjdilM+pTY1Tk91o\npNbMNa1n1WSS7ulNZ1KmpabUqKUnlZOaMqoWXsvI0npGC2V+zhSdoa/2pNKc9Dn097Uv0X0r7vM6\nmLPTvE708OqHadLOSfTJji9p34l9dCD7kuV0zchjVaEzO3VGK12nyckxnjqm96yREmpVgTdbX8yU\nvda7rmXsqL03gVaQfEnLqIyNDArpmjKM3rtsxmjyVXYJs0aw07+bnXZazXhwGrvvrFZfaOZ39QeB\ner/8nY7d+PUM6mD9JmYIBxkrG2zsOMCVc+fo0MYMGnXzGtr10kf0SdsXad99Ayij0y3eBk2LlrSx\nWQx93rAhTbjuz/RY3XrUvXZtiqlenepE/oGI3LuqCVF2Js6gQcPLdQ5G0zX0DBfltDQ1z43ZuOSj\n3kpjQu4tUlOSpGl0Vjt8M+GVCrdy+oEZZdGOu9+Kcm80QmgmrmPnj9Hmw5tpyuaP6bn/Pkd3LrnT\na31N6xl/pSErn6LXNr5Ha7PXUs7pHM/BnEYGly/YyVMgR0zNYlb5UXvOjIFsZ2qWGUXSaA2KXqer\nN53NF6PcLP76/e0Yf8r7RmWmp/j7s+y06psvbYvV9O08E4qL3vUGKZThrMRZmfFVyTdrUIdaOYeD\njJUJNnZ8oKSkhLLu6FHOoMnsciu5Bg6iA8+NpgltEyjniy+ocN8vdOXCBTpy5Ajde+8AEqLsTJy+\nfQdS584FtGwZUd26STR48NO0Z88eGjz4aUpKSvIyOvQUJL3RvJwc95qZ+Hj1ba+JyntkJKVJS/GR\nn6JtZucziW3bymRRyqHVQMjPHTLTiCg7La0yk0/pk8ejZeiYUVD1rm3b5l7P0bateWX0SskVyjmd\nQ2uz19J7u96jp9Y/RX9d+Fcvw+bOJXfSc/99jj7+38e0+fBmOnb+WLkyUO7uZsWAtBLOTJ7M3gs2\nasqPL1Oq7ISxYuyoPWM17WApob7UA7PGp5k4tAwTu3U3EPXbrJJuNU4zYXzZKtvMRh5W678vmG0T\nzRqvodq2BVqeUMs/U/lgY8dHjr47jY5/+BGdXrOGDm3MoB5dz3mUhOHDh1OVKlVo+PDhXorDsGFu\n740QkSRE2f1OndTPbLEyx1+5AYBcQZcf2qlES7nXmzOuVOK15FBLS25ESd/la3qU4fWm2ul1uGaU\nkEmTiCIi1I0BZd71NgFQC68Vh5Zcl65con0n9tEnO76kSTsn0cOrH6ZO8zp5jJq2KW0paUUSvbTl\nJZqdPptSC1LpTNEZw3wuW1aWRz382WnrKe5W4nXKkLCCsk4EyhMlL3enFCczzwdrtN1OnTBrcJiJ\nQ9ne+duwdfq3dOp302rL1MKZ8dCbbRuV6fij/luR0Uw4o+f0+i2r8jlBqBpgDONP2NhxmG3biKpU\nUT8Tp3p195k4SUlJ9PTTT9OaNXvo6afd3hsi/UbRrIIi92RIO0IZGUp6BpWagmp2io4VBUGKTymD\nGSNP3uGaHXFT3tfbpEH52ayHyUj285fO0+6ju2l+5nwa//146reyH7Wb3c5j2HSc25EGfj2QXt/+\nOi3+ZTHtPb6XLhZfLBeP2aloRgeUKhV5vbzZwUhxMVNfzNQts+VhNz+S/E6suTKbntm4rb7rwUSr\n3Owotk4Yy0oD1uh98EUGefx2DSmrSrfVNJRTmq0MKNm9ppaO/LO/jXCr9U3rfTMy6uzU70B77SoC\nXF6MBBs7PnLkSNmZOFInnZp6hHr0GEBA2VS1xMSBXufmmEHekCoXv6s1rsoRZ72pUkrUFvBrNbB6\nip6afGpGjFoelXmxsoWpmrxOdBBacRkpuMrwJwtP0vf539OnP39KYzaNoXuW3UOtZ7X2GDZdF3Sl\nQSueoHd+eIe+Pvg1HTx10OtgTq38WlXItOIyuyZMLw61z2rhlEqttP252qYWSvmUcajFbaY8fFE0\npfdRbYqnnTrnREeqzLueERYKHbfWu6q3uYk/0jVz3Zd4jZ6xasyohdH7roVyZ0619lqZttk1lmbk\n1ntW71DeUDHUiazVJbPGn1F6dp/1FTvpBft3cqq+hFq9Y+zBxo6PSFPVBg0a7rUNszRVrUYN91S1\nBg2GW1JIlcqf2rbNZhRuvfiVaaktTtbrdJRbXWuFVW6CYKTgEHmf+2AmD1rXfDV0zHTacvlLSkoo\n/2w+bcjZQGNXTqeRG0ZS90XdvdbX9Fjcg5759hmasXsGbczZSAXnCsjlKrFk2MlPOXdKWVZ+tqPo\nGXUMyrCxse7pdfLznrR2E1SLQ/qujNdqno3kVLuvd89sWnY7Uq3yUPvttJRaszL6CyNjTM9480X5\n0vstzMah9t2qUm91HZFTCpfk9Ze2yle291p1yUq5+SK3UXxW8Hed9qUu+ZJmIJVvu7+hLzI61Y87\nVUZs6IQ/bOzYJDJSfapajRreU9X27HFPVfvrX5MsKbPK/0rFypcOQWmsEKmfo6MWt1pa8fHuP7kn\nSR5ebftqtXiUyo3RtCsnFCAj9OI85CqmLRkHqNMjq+i5FVPp8bWPU5e5t5QZNp+1od6L+tDzm5+n\nz3/+nLYf2U6/F/5uKy3lfbMKrBX0DAqrz1oJq1xHZMZgUVOmnJ7iome8WSl/tfdNed/MNWV8Zso8\nJ8fbMLYSpxlZ/I2W8WbVc+CEgqiWrt04jeq4v38H5c6cWukY1Qujuq2Mz+59uwMCVr114aLUBntg\nwl/PSM8pB1PZs8L4Chs7Njly5AgNGDCAatYsm6p27bUD6Ycfyk9V0+qw1ZA2EpAbOHqL86W4rJxV\nIoVXxiv9Vxo88sZGGe+2be4RQvlW0modoNkRMKUMavKpPWunYzMTRrp3sfgi/Xz8Z1r0yyJ6b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yE1+oena0vBjyER35yJ3kbXC5SqjgXAFtzNlIkzbNoGe+fYa6Lejhtb7mtgXdqfUrI2nipum0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\n",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imp.reload(bv)\n",
    "bv.plotLinearBiasStage(20, b, 5*np.array([2**i for i in range(12)]), (14, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>The blue dots represent the data we are trying to model. Each line is a linear model fit to a different data set from the data generating model (same as above), but with increasingly more points. You can see that most of the lines cluster together, and that the line fit with the most points is right in the middle there. The starred, black-curve running through the middle is the true mean of the distribution (i.e., $E[Y|X]$ if you were to have infinitely many data points to model). We can see that even the linear model fit on the most data is far away from this true mean at various points. If we extended the simulation and modeled with infinitely large data sets, nothing would change this fact: a linear model will never truly fit the curvature of the data. This is an example of model bias. Because this is a regression problem, we can even quantify this bias: <br><br>\n",
    "\n",
    "A linear model will take the form:<br>\n",
    "<center>$E^{d=1}[Y|X=x]=\\hat{\\beta} *\\:x$</center><br>\n",
    "\n",
    "But we know that our data follows the form $Y=\\beta^T \\: K^d(X) + \\epsilon$ and a properly specified model would give the following estimate:<br><br>\n",
    "\n",
    "<center>$E[Y|X=x]=\\beta^T \\: K^d(X)$</center><br>\n",
    "\n",
    "The bias of the linear model is the difference between its estimate of $E^{d=1}[Y|X]$ and the true $E[Y|X]$, which is:<br><br>\n",
    "\n",
    "<center> Bias(d=1) = $|E^{d=1}[Y|X=x] - E[Y|X=x]| = |\\hat{\\beta} *\\:x - \\beta^T \\: K^d(X)|$</center><br>\n",
    "\n",
    "Bias is not a quantity that you can reduce by adding more data (directly at least), because it is a property of the type of model you have chosen a priori to fitting the data. The expecations above are take over the true probability distribution $P(X,Y)$ and have nothing to do with the actual training data sample (and specifically, the quantity of it). The consequence of this fact is that adding more data without changing the form of the model won't make any bias disappear (if it is to exist at all). Thus, one can't reduce any error caused by bias if we can't change the model structure we are using. <br><br>\n",
    "\n",
    "<i>So why not just make models as flexible as possible?</i><br><br>\n",
    "\n",
    "This is a perfect segueway for discussing variance in model estimation.\n",
    "</p>\n",
    "\n",
    "#### Deeper Look at Variance\n",
    "\n",
    "<p>\n",
    "\n",
    "Core machine learning theory focuses a lot of attention on how flexible a particular type of model can be. The best algorithms (in terms of accuracy) are usually the ones that can fit any arbitrary function that may represent the classification or regression task. Of course, with great power comes great responsibility. When we seek to reduce bias by using more sophisticated modeling algorithms, we have to be mindful that a lot of algorithms are really good at fitting the noise in our data. With smaller data sets, the models have a harder time distinguising inherent noise from signal. Variance in this context is thus intimately linked to the size of the data set.<br><br>\n",
    "\n",
    "An important concept for understanding model variance is to define the variance of 'what' over 'what.' Remember again that a model is our best estimate of $E[Y|X]$, and we use a given training set $j$ to learn this estimate. Every training set though is just some sample over a larger, hypothetical distribution $D$ (that in practice we generally never know). Now imagine a set of parallel worlds, each having some different (but similar) dataset $j$ sampled from $D$.  Now further imagine that we estimate a different $E^j[Y|X]$ over $D$ in each world, using some algorithm with a fixed structure. Because of the random permutations and differences of the different training sets, we should expect each $E^j[Y|X]$ to be slightly different than the next. Exactly how different is the appropriate question here. More flexible models will likely produce more varied estimates, and this variation will increase as sample sizes decrease. So in short, the variance we refer to here is $Var(E^j[Y|X])$, over possible different permutations of the data set. <br><br>\n",
    "\n",
    "The following illustrates this idea using the simulation example from above. In this case we compare a biased (linear) model to a zero-bias model with different sized training sets. We simulate the parallel world concept and plot the results of the model trained on each 'world.'\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
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XV9k8bIkUQjGPB0jWuSW6S/rnTSbr3Pr7CR85UrZ1buWWEpkvFRfIDQ0NJWve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\nnb55va+/1G233caLL76Y75c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RydXk5OSaPk8LRiLlreICufn5+VIPYU0CgUDGIC7bAK61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N9/b2KHFvt9vI5/PQ6/XUsUulUtSd7aMT4XD4RBLH5P170S7Vh4eY+exnod7fx87f+3s4+MhH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\niIiIiIhERgM5ERERERGRyPwf/5zhByFFxGAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1500x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imp.reload(bv)\n",
    "bv.plotVariance(20, b, [10, 50, 100], (15, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>This chart paints several trends around the idea of model variance. First though, let's arm ourselves by actually defining model variance. For a particular value of $X=x$:<br><br>\n",
    "\n",
    "<center> Model Variance = $E_D[(\\hat{Y}_x - E_D[\\hat{Y}_x])^2]$</center><br><br>\n",
    "\n",
    "For notational convenience, we define $\\hat{Y}_x = E_j[Y|X=x]$, where $j$ indicates a particular data set (i.e., this is the estimated model fit for a particular value of $X=x$ on a particular instance of the training data $j$). The notation $E_D[.]$ means we are taking an expectation over all possible datasets $j$ of size $N$ that are sampled from $D$. In each chart above, the red line is the avg. of $\\hat{Y}_x$, which is an unbiased and consistent estimator of $E_D[\\hat{Y}_x]$. Each grey line represents the model estimated from each training set $j$ in our simulation. The quantity $E[std|X]$ represented in the title is the avg. standard deviation (square root of variance) across all values of $X$ in the data. <br><br>\n",
    "\n",
    "With the core concepts and charts defined, we can focus on the interesting trends shown above. Visually we can see that the variance increases as we switch from the biased linear model (top row) to the unbiased polynomial model (bottom row). We can also see that the variance decreases left to right for both model classes, which is caused by adding more data to each model. So in short, model variance tends to increase under two scenarios: 1) the model you are fitting is more flexible or complex, 2). sample sizes decrease. Intuitively, this should make sense. More flexible models can essentially go through more points. The problem though, is that a lot of the points are just noise, and so in any one realization of the data, the model has less evidence to distinguish whether the point is signal or noise. A biased and inflexible model doesn't care as much about noise - it doesn't have what it takes to fit the noise. <br><br>\n",
    "\n",
    "One subtle but important item to point out here is that the dispersion of the models (grey lines) around their average is not equal for all values of $X$. In particular, we can see more dispersion around extreme points. If you recall, the data generating process had an error term with equal variance for each value of $X$ (homoscedasticity is what we call that). So how do we end up with extra variance at the extreme values? This is a subtle but important point that happens a lot in modeling. In most cases, data is not uniformly distributed around $X$, so extreme ranges tend to have less support. Since we defined model variance above conditioned on $X=x$, what matters more is not the total sample size, but the number of instances where $X=x$. It turns out that model variance is often higher where $P(X=x)$ is lower. This technically doesn't explain the results above though, because the data was generated uniformly random on $X$. In a related, but different way, the extreme values have more variance because each extreme value has fewer points near it. Parametric models (which is what we have here) \"borrow\" information from neighboring points to get a good fit. When a point has fewer neighbors (which is the case at the boundaries), there are fewer neighbors to exploit. <br><br>\n",
    "\n",
    "So how much data is required to reduce or at least stabilize the variance? The exact sample size is heavily problem, model and data dependent, but we can at least extend the above simulation to show the basic trend. \n",
    "\n",
    "\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+NrP5ZtbVzNSqmEccX+x4bml8C5O/nsx3274LO46IiIiIiMRIRkXcw8BE4BTn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\n",
      "text/plain": [
       "<Figure size 1500x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imp.reload(bv)\n",
    "res = bv.getVarianceTrend(20, b)\n",
    "bv.plotVarianceTrend(res, (15, 9))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>This plot should confirm several of our expectations. First, when the complexity of our models increase (measured by the degree of the polynomial), model variance increases. Also, variance decreases as we increase the sample size. Beyond that, note the shape of the curves in the top plot above (for any model degree). We can see that doubling the data (each tick on the x-axis represents a doubling) cuts the variance in half. For instance, with $d = 4$ (our one unbiased model family), going from $2^{10}$ to $2^{11}$ records drops the variance around 50%. The bottom chart shows both the sample size and model on the log2 scale. We can see that relationship on the log-log scale is nearly perfectly linear. Every unit increase in log2 scale decreases the log2 of the variance by a constant amount, such that: $log_2(\\hat{\\sigma_m}^2) =  -log_2(N) + c$. With a little algebra we get to $\\hat{\\sigma_m}^2 \\propto N^{-1}$. Here we have shown empirically what is well known analytically - that model variance reduces at the rate of $O(N^{-1})$. For reasons that we'll discuss later, investing in more and more data to reduce the variance doesn't always pay off (in terms of reducing error to balance out the additional data cost).\n",
    "</p>\n",
    "\n",
    "## Model Error, Bias and Variance\n",
    "\n",
    "<p>So at this point we should have a solid foundation as to what exactly we mean by a model's bias and variance. And most importantly, we should now be able to understand the drivers of model bias and variance. Understanding such drivers gives the modeler an element of control over the two phenomena. But why do we need to control such things? After all, our modeling goal is generally not to hit some target level of bias or variance. Rather, we want to reduce the prediction error of our model. It turns out, bias and variance are intimately connected to model error, such that the total model error is often explicitly determined by the amount of bias or variance. Thus, controlling bias and variance in essence helps us control the error.\n",
    "</p>\n",
    "\n",
    "### Bias-Variance Decomposition for Least-Squares Error\n",
    "\n",
    "<p>\n",
    "The most famous and convenient representation of this relationship is done using least-squares error. For problems in which MSE (least squares) is our error function, we can express the MSE explicitly in terms of model bias, model variance and the underlying noise in our target data (which is often called the 'irreducible error', for reasons discussed later on). In this section we'll derive this famous decomposition.<br><br>\n",
    "\n",
    "<b>Important Concepts Defined</b><br><br>\n",
    "\n",
    "First, let's assume our data comes in pairs $(X, Y)$, and our general estimation goal is to learn a function $f(X)$ such that $f(x) = E[Y|X=x]$. Because our data is finite, we never know the true $E[Y|X=x]$, but we can estimate $f_j(x) = \\hat{Y}_x = E_j[Y|X=x]$ over some fixed, finite sample $j$.  Our MSE at a give value of $X=x$, with known $Y=y$ is defined as:\n",
    "\n",
    "<center>$MSE(x, y) = (y - \\hat{Y}_x)^2$</center><br><br>\n",
    "(note, for notational simplicity we ignore the fact that MSE is often computed and averaged across all instances of the data. The derivation still holds for a single instance). <br><br>\n",
    "\n",
    "Before we continue, we need to introduce and explain some important concepts and quantities. <br><br>\n",
    "\n",
    "<ul>\n",
    "    <li><u>Family of functions $\\mathbb{F}$</u>: This is a finite set of functions that share some structural properties. For instance, all decision trees on a set of features with depth=10, or all linear functions of the form $f(X)=\\beta^T \\: X$. Generally any classification algorithm can be placed into some particular family $\\mathbb{F}$.</li><br>\n",
    "\n",
    "    <li><u>Expected vs. Empirical risk:</u>The expected risk is a theoretical property we could know if we knew the true distribution $P(X,Y)$. For MSE the expected risk of a given function $f$ is $E[MSE(f)]= \\int ((y-f)^2 \\: dP(X,Y)$. Since we generally don't know $P(X,Y)$, we have to approximate the expected risk with the empirical risk, which is defined as: $E_j[MSE(f)] =n^{-1}\\sum ((y_i - f_i)^2$, where the sum is taken over some finite data set $j$.</li><br>\n",
    "\n",
    "    <li><u>The true risk minimizer $f^*$</u>: This is the function $f^*$ that best approximates the true $E[Y|X=x]$ and is the one that minimizes the expected risk across all families of functions. It can be defined as: $f^*= \\underset{f} {\\mathrm{argmin}} \\int((Y-f)^2 \\: dP(X,Y)$. In other words, this is the absolute truth that we wish we knew, but generally have to approximate using a finite data sample and some estimation procedure.</li><br>\n",
    "\n",
    "    <li><u>The expected risk minimizer from family $\\mathbb{F}$,   $\\:\\:f^{\\mathbb{F}}$</u>:This is similar to the true risk minimizer, only that we restrict the exploration of candidate functions to only those included in $\\mathbb{F}$. The exact definition is: $f^{\\mathbb{F}}= \\underset{f \\in \\mathbb{F}} {\\mathrm{argmin}} \\int((Y-f)^2 \\: dP(X,Y)$. One way to think about this is, using the above example, imagine we had infinite data but could only fit some non-linear data scatter with a linear model - the result wold be the expected risk minimizer from the family of linear functions on $X$.</li><br> \n",
    "\n",
    "    <li><u>The emprical risk minimizer from family $\\mathbb{F}$,   $\\:\\:f_j^{\\mathbb{F}}$</u> This is similar to the above definition, but using the empirical risk definition as opposed to the expected risk definition. When we fit a model on finite data, this is generally the function we end up with.</li>\n",
    "</ul><br><br>\n",
    "\n",
    "\n",
    "<b>Derivation of MSE Bias-Variance Decomposition</b><br><br>\n",
    "\n",
    "So now we have 3 functions defined, $f^*$, $f^{\\mathbb{F}}$ and $f_j^{\\mathbb{F}}$. We'll first rewrite MSE using this new notation:$MSE = (y - f_j^{\\mathbb{F}})^2$. Also, as our goal is to generalize to new data, we care more about the expected MSE, or $E[(Y - f_j^{\\mathbb{F}})^2]$. What we'll now do is use algebra and some statistical rules to decompose this expected MSE into more familliar terms:<br><br>\n",
    "\n",
    "<center>$E[(Y - f_j^{\\mathbb{F}})^2] = E[Y^2] - 2E[Y*f_j^{\\mathbb{F}}] + E[(f_j^{\\mathbb{F}})^2]$</center><br><br>\n",
    "\n",
    "Using the rule that $E[X^2] = Var[X] + E[X]^2$, and a little algebra, we can expand this to:<br><br>\n",
    "\n",
    "<center>$E[Y^2] - 2E[Y*f_j^{\\mathbb{F}}] + E[(f_j^{\\mathbb{F}})^2] = Var[Y]+E[Y]^2 + Var[f_j^{\\mathbb{F}}]+E[f_j^{\\mathbb{F}}]^2- 2E[Yf_j^{\\mathbb{F}}] = Var[Y]+ Var[f_j^{\\mathbb{F}}] + (E[Y] - E[f_j^{\\mathbb{F}}])^2$</center><br><br>\n",
    "\n",
    "Now, according to our definitions above $Y = f^* + \\epsilon$, giving us $E[Y] = E[f^*] = f^*$., and $E[f_j^{\\mathbb{F}}] = f^{\\mathbb{F}}$ (remember, the expectations are taken over the true distribution so these terms are deterministic). Also, since $f^*$ is deterministic and independent of $\\epsilon$, $Var[Y] = Var[f^*] + Var[\\epsilon] = \\sigma^2$. Putting this all together, we get <b>the punchline of all of the above:</b><br><br>\n",
    "\n",
    "<center>$E[(Y - f_j^{\\mathbb{F}})^2] = \\sigma^2 + E[(f_j^{\\mathbb{F}} - f^{\\mathbb{F}})^2] + (f^*-f^{\\mathbb{F}})^2 = Irreducible Error + Variance + Bias^2$</center><br><br>\n",
    "\n",
    "Getting to this point was quite a feat, but now we have a great framework for understanding how and why our models may be wrong. Note that although we were able to derive an exact analytic bias-variance decomposition for MSE, other loss-functions (LogLoss, MAE, etc.) don't provide us with the same luxury. Nonetheless, the general framework is the same, and what we learn qualitatively from the MSE case can be applied to the Non-MSE case.\n",
    "</p>\n",
    "\n",
    "## Improving Models Using the Bias-Variance Framework\n",
    "\n",
    "<p>Understanding the bias-variance decomposition isn't just a theoretical exercise. Quite the opposite, it is the most fundamental principal that governs model generalizability and its lessons are directly applicable to empirical problems. Here are some key takeaways that we can highlight based on the above analysis that can be directly applied to our work. <br><br>\n",
    "\n",
    "<b>More data should not hurt, and is likely better:</b><br><br>\n",
    "This is probably an obvious statement based on everything we hear about \"Big Data,\" but now we have a framework to understand why. Having more data examples reduces model estimation variance at the rate of $O(N^{-1})$, which all else being fixed (remember, more data doesn't change the bias or irreducible error given a fixed model family), reduces the total error. An important caveat is that the value of this variance reduction might not be proportional to the costs associated with increased data. This is a very problem specific trade-off that should at least always be considered (remember, data costs can be in actual economic currency or CPUs). Also, having more data doesn't guarantee measurable results. There are always diminishing returns, so the improvement will depend on where you are on the sample size vs performance curve.\n",
    "\n",
    "<b>Model complexity is your friend, but it can stab you in the back if you are not careful:</b><br><br>\n",
    "The bias part of the error decomposition is purely a function of your model specification and is independent of the data (specifically, the number of examples, not features, in your data). One can often get better results by using more complex (flexible) modeling algorithms. Example ways to add model complexity are: adding new features to the dataset, using less regularization in linear models, adding non-linear kernel functions to SVMs, using Neural Networks or Decision Tree based methods over linear methods. <br>\n",
    "\n",
    "The major caveat is worthy of its own paragraph here. Given a finite data set, adding more complexity will almost always increase the estimation variance of your model. As discussed above, more flexible models will confuse more noise (the $\\epsilon$ part of $Y=f^*+\\epsilon$) for signal. Thus, we have to be very careful here. Adding more data given a fixed model family might waste time, money or CPUs, but it should't make your model worse. Adding more complexity can really hurt you if not done carefully. So this begs the questions:<br>\n",
    "\n",
    "<i>How much model complexity should I use?</i>\n",
    "\n",
    "<ul>\n",
    "<li>This is ultimately an empirical question, as the truth is never known outside of carefully crafted simulations. This is why we ALWAYS use train/valiation splits or cross-validation to guide our model selection. While we have a good analytic model for knowing how increased sample size will decrease variance, we don't have the same for predicting how increased complexity will increase it. Thus, we have to carefully test our models on out-of-sample data to find the empirically optimal trade-off. <br>\n",
    "</li>\n",
    "</ul>\n",
    "\n",
    "\n",
    "<i>How do I know if my model is suffering from too much bias or too much variance.</i> <br>\n",
    "<ul>\n",
    "<li>The best performing model on out-of-sample data again will be the one that is 'just-right' in terms of its bias and variance. Generally, the sweet-spot will be the level of complexity in which the testing error is the lowest. Given a particular model fit, we can diagnose whether it suffers from high/low bias/variance buy comparing both the training and the testing error. If the training error and testing errors are both high, then bias is the likely culprit. If training error is low but testing error is high, then variance is likely the culprit. The following chart (taken from the book Elements of Statistical Learning 2) shows a hypothetical example. In a realistic case, the x-axis will be defined by whatever parameters define complexity for your choice of algorithms (i.e., the regularization weight, depth of trees, number of trees in a forest, etc.).\n",
    "\n",
    "\n",
    "</li>\n",
    "</ul>\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Jkvwt9UD6iIyjLI/sJ7l6+TgFdRJE6lLMVbJUZlAeUilVdVXjUhtBssBNg01jWPO4lqu2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j54RgMwETMIFCIPD222+jQYMGOPpo5TLZopHArifjaGyd22QCJlB0BFJW4afhP4YertWIWk17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\nBEiABEiABEiABEiguhD4P8n+UclKZHP8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='references/bv_complexity_es2.png') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<p>\n",
    "<b>In most observed phenomena, you can never get perfect predictions. Also, you shouldn't really compare models across different problems</b><br><br>\n",
    "\n",
    "These are two different statements, but they're both related to that $\\sigma^2$ term, which again we call the irreducible error. The first statement should be obvious if we assume $\\sigma>0$. Almost all systems we attempt to model, especially systems that capture human dynamics, are burdened with noise. With an infinite sample size we can potentially test every possible function and arrive at both zero variance and zero bias. Even then, the quantum mechanical nature of the universe, and the fact that there is always likely some unmeasured variable (whose effect can be folded into $\\epsilon$), means that $Y$ will always have some unexplainable variance. No model and modeler will ever get beyond that point (for those interested, this error is often called the 'Bayes Error').<br><br>\n",
    "\n",
    "The second statement above is related to the fact that every $Y^p$ will have an associated $\\epsilon^p$. A model predicting $E[Y^1|X=x]$ may have both lower bias and variance than a model predicting $E[Y^2|X=x]$, but if $Var[\\epsilon^1]>Var[\\epsilon^2]$, the model on $Y^1$ can still have a worse error than the one for $Y^2$. Generally this isn't a problem, so long as the appropriate comparisons are made. Specifically, the qualitative assessment of a model should be relative to a baseine derived from the same problem (i.e., predicting the same $Y^p$). And in some circumstances, a model that does 5% better than random on some problem may be worth a lot more money than a model that is nearly perfect (think about predicting stocks vs. predicting sunset). <br><br>\n",
    "\n",
    "Corrollary to the above paragraph is the following rule: don't judge a model just because its error is too close to random. Again, every model should be judged using appropriate baselines that are specific to the problem at hand. Accuracy might be far from 100%, but the model can still be good (and this relates back to how much irreducible error is in the underlying system).\n",
    "</p>\n",
    "\n",
    "## Wrap Up\n",
    "\n",
    "<p>At this point you should (hopefully) understand the following at both an intuitive level and foundational theoretical level:\n",
    "\n",
    "<ul>\n",
    "    <li>Model Bias</li>\n",
    "    <li>Model Variance</li>\n",
    "    <li>The relationship between bias, variance and prediction error</li>\n",
    "    <li>How to simulate and represent graphically model bias and variance</li>\n",
    "    <li>How to use the bias-variance tradeoff to guide your modeling decisions</li>\n",
    " </ul><br>\n",
    "\n",
    "<b>references</b><br><br>\n",
    "\n",
    "The presentation here was intentionally light on theory and mainly focused on the bias-variance decomposition of MSE. For further exploration of the topic, feel free to consult the following:<br><br>\n",
    " \n",
    "<ul>\n",
    "    <li><a href=\"https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff\">Wikipedia</a></li>\n",
    "    <li><a href=\"http://statweb.stanford.edu/~tibs/ElemStatLearn/\">The Elements of Statistical Learning 2 (section 2.9, chapter 7)</a></li>\n",
    "    <li><a href=\"http://papers.nips.cc/paper/3323-the-tradeoffs-of-large-scale-learning.pdf\">Leon Bottou's work on the Tradeoffs of Large Scale learning (section )</a></li>\n",
    "    <li><a href=\"http://homes.cs.washington.edu/~pedrod/papers/mlc00a.pdf\">Pedro Domingo's General Framework for the Bias Variance Tradeoff</a></li>\n",
    "</ul>\n",
    " \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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